DOI QR코드

DOI QR Code

Validation of the effectiveness of AI-Based Personalized Adaptive Learning: Focusing on basic math class cases

인공지능(AI) 기반 맞춤형 학습의 효과검증: 기초 수학수업 사례 중심으로

  • Eunae Burm (Division of Nursing, Baekseok Culture University) ;
  • Yeol-Eo Chun (Division of Nursing, Baekseok Culture University) ;
  • Ji Youn Han (Division of Dental Hygiene, Baekseok Culture University)
  • 범은애 (백석문화대학교 간호학과) ;
  • 전열어 (백석문화대학교 간호학과) ;
  • 한지연 (백석문화대학교 치위생과)
  • Received : 2023.03.19
  • Accepted : 2023.04.30
  • Published : 2023.06.30

Abstract

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

본 연구는 AI 기반 맞춤형 학습 시스템을 시범적으로 운영하여 대학 수업에서의 AI 기반 맞춤형 학습 시스템의 적용 가능성과 효용성을 알아보고자 하였다. 이를 위하여 C지역 소재 B대학교 1학년 재학생 중 기초수학 교과목 수업에 참여한 42명 학습자를 대상으로 AI 기반 맞춤형 학습 시스템을 적용 및 운영하였고, 학생 및 교수를 대상으로 설문 문항 조사와 인터뷰를 진행하였다. 연구 결과, AI 기반 맞춤형 학습 시스템의 활용은 학생의 학업성취도를 향상시켰다. 심층인터뷰 결과 교수자와 학습자 모두 기초 개념 학습에 있어 학습 성과 향상에 기여하는 것으로 파악되었다. 이는 AI 기반의 맞춤형 학습 시스템이 자기 주도 학습의 역량을 향상하고 개념학습을 통해 지식 강화에 효과적인 방안이 될 것임을 시사한다. 본 연구는 인공지능 기반 적응형 학습 시스템의 기초 과학 교과목 도입과 적용에 관련한 기초자료로 활용될 수 있을 것이다. 향후 AI 기반 맞춤형 학습에서 학생들에게 제공한 학습과정과 분석한 데이터를 대면수업에 연계한 효과 검증과 분석한 데이터의 활용 방안에 대한 전략 연구를 제언한다.

Keywords

References

  1. J.H.Shin, and J.E.Shon,"Analysis of Faculty Perceptions and Needs for the Implementation of AI based Adaptive Learning in Higher Education,"Journal of Digital Convergence, Vol.19. No.10, pp.39-48, 2021.
  2. M.Chung, and Y.Yang."A study on basic learning ability support system for university students: Based on professors and students' perception and needs," Journal of Education & Culture, Vol. 22, No.2, pp.101-126. 2016. https://doi.org/10.24159/joec.2016.22.2.101
  3. J.Lee." A case study on basic learning ability achievement in the field of basic mechanics for students with poor basic learning ability," Journal of Practical Engineering Education, Vol.10. No.2, pp.95-102. 2018.
  4. M.Brown, M.,McCormack, J.Reeves, C.Brooks, and S.Grajek," EDUCAUSE Horizon Report," Teaching and Learning Edition. Louisville, CO: EDUCAUSE. 2020.
  5. M.M.Tesene. "Adaptable Selectivity: A Case Study in Evaluating and Selecting Adaptive Learning Courseware at Georgia State University," Current Issues in Emerging eLearning, Vol.5. No.1, Article6. 2018.
  6. J.H.Shin, J.W. Choi, S.Y. Park, J.E. Shon, E.K. Hwan, S.H. Ahn, and S.I. Kim, "An exploratory study on the use of AI-based adaptive learning system in university class," The Journal of Educational Information and Media, Vol27, No4, pp.1545-1570, 2021.
  7. Education commission Asia[Internet], https://educomasia.org/
  8. L.N.JIN, "Recommendation System Design Based on Learning Style: Focusing on AI Language Learning Applications," , Master's Thesis, Ewha Womans University. 2021
  9. E.K.Hwang, and J.H.Shin,"Exploratory Study for Introducing and Applying an AI-based Intelligent Learning System on Basic Science-Focusing on General Chemistry Class Case" 2021, Korean Journal of General Education, Vol.15, No.6, pp.71-86, 2021. https://doi.org/10.46392/kjge.2021.15.6.71
  10. M.M.Tesene, Adaptable selectivity: A case study in evaluating and selecting adaptive learning courseware at Georgia State University, Current Issues in Emerging eLearning. Vol.5, No.1, pp.62-79, 2018.
  11. Tyton and Babson Survey Research Group, Making the Case for Courseware. Everylearner Everywhere. 2021.
  12. K.Vignare., E.C.Lammers., J.Greenwood., T.Buchan., M.Tesene., J.DeGruyter., D.Carter., R.Luke., P.O'Sullivan., K.Berg., D.Johnson., and S.Kruse, A guide for implementing adaptive courseware: From planning through scaling. Joint publication of Association of Public and Landgrant Universities and Every Learner Everywhere. 2018
  13. S.Oxman and W.Wong. White paper: Adaptive learning systems. Integrated Education Solutions, 6-7. 2014.
  14. Y.C.Cho, "Effects of AI-Based Personalized Adaptive Learning System in Higher Education," Journal of The Korean Association of Information Education, Vol.26, No.4, pp.249-263, 2022. https://doi.org/10.14352/jkaie.2022.26.4.249
  15. J.S.Lee, K.B.Moon, S.Y.Han, S.K.Lee, H.J.Kwon, J.H.Han, and G.T.Kim, "Development and Application of an AI-Powered Adaptive Course Recommender System in Higher Education: An Example from K University" Journal of Educational Technology, Vol.37, No.2, pp.267-307, 2021. https://doi.org/10.17232/KSET.37.2.267